Improving Accuracy of Isolated Word Recognition System by using Syllable Number Characteristics
Published at : 21 Apr 2020
Volume : IJtech
Vol 11, No 2 (2020)
DOI : https://doi.org/10.14716/ijtech.v11i2.3678
Hidayat, R., Winursito, A., 2020. Improving Accuracy of Isolated Word Recognition System by using Syllable Number Characteristics. International Journal of Technology. Volume 11(2), pp. 411-421
| Risanuri Hidayat | Department of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada, Jl. Grafika No. 2, Yogyakarta 55281, Indonesia |
| Anggun Winursito | Department of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada, Jl. Grafika No. 2, Yogyakarta 55281, Indonesia |
Studies are constantly developing and improving speech
recognition systems, especially their accuracy. This study developed an isolated word recognition system by using the
syllable number characteristics of speech signals that will be recognized.
First, the syllable number of speech signals to be recognized was detected, and
then, the detection results were used to call one of the database groups that
matched the syllable number characteristics. This method was designed to reduce
the error possibility through a matching process between test data features and
database features. This study used Mel frequency cepstral coefficients (MFCC)
for feature extraction and the K-nearest neighbor (KNN) method for
classification. Three versions of the proposed method were designed. The
results showed that version three increased the accuracy by 4% compared to the
conventional recognition system. Version three had the fastest computational
time compared to the other methods. The addition of syllable detection
algorithms in version three increased the computational time by only 0.151 s
compared to the conventional MFCC method. The data cut length and threshold
value for the filter also influenced the speech recognition system accuracy.
Isolated word; K-nearest neighbor (KNN); Mel frequency cepstral coefficients (MFCC); Number of syllables; Speech recognition
Technology plays a crucial role in human daily life and is developing at a rapid rate. One such technology is speech recognition. Speech recognition technology is being widely used in applications such as mobile phones, home security systems, and global positioning systems. Studies have used speech recognition systems to recognize drones (Shi et al., 2018). Studies on speech recognition systems are continually improving the recognition results. Speech recognition systems use several main stages including feature extraction and classification to identify speech patterns (Dahake et al., 2016). The feature extraction process obtains the characteristics of a sound frame, and the classification process chooses a word by analyzing extracted features (Jo et al., 2016). Mel frequency cepstral coefficients (MFCC) are widely used for feature extraction. Some studies have already used MFCC for feature extraction (Adiwijaya et al., 2017; Vijayan et al., 2017; Hidayat et al., 2018; Kumar et al., 2018; Marlina et al., 2018; Winursito et al., 2018; Li et al., 2020). Although MFCC is widely used in speech recognition systems, they still require further development, especially in terms of their accuracy (Winursito et al., 2018).
Several studies have tried to improve
the performance of the MFCC method. One study improved the MFCC method by
adding a delta coefficient (Hossan et al., 2010) and compared this MFCC + Delta method with the ordinary MFCC method.
The results indicated that the added delta coefficient improved the speech
recognition system’s accuracy. Another study (Hidayat
et al., 2018) added a wavelet-transform-based noise
reduction system. This is because the MFCC is quite susceptible to noise
interference in the sound input, and this impacts the speech recognition
system’s accuracy. Other studies used wavelets and a psychoacoustic model for
speech compression (Gunjal and Raut, 2015). A study on noise removal in speech signals (Tomchuk, 2018) tried to realize high speech recognition
system accuracy for both signals with and without noise. A recent speech
recognition system added a data compression method (Winursito et al., 2018) and compressed the
total output data of all MFCC features by using a principal component analysis
(PCA) method. Data compression was performed for removing unnecessary data and
leaving behind only important data. The study results indicated increased
accuracy at the cost of increased computational time.
The
development of speech recognition systems by using syllable number
characteristics improved the speech recognition accuracy. Version three of the
proposed method improved the speech recognition accuracy by 4% compared to the
conventional MFCC method. This method was developed by dividing the reference
database into two parts based on the syllable number characteristics. In
developing a recognition system using the proposed method, the speech
recognition system accuracy strongly depends on the syllable number detection
accuracy. That is because if the system incorrectly recognizes the syllable
number, the classification process will use a wrong database and the word
recognition will also be wrong. The data cut length and threshold values also
affected the speech recognition system accuracy. Version three had the fastest
computational time compared to other methods. The addition of syllable
detection algorithms to version three of the proposed method only increased the
computation time by 0.151 s compared with the conventional MFCC method.
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| R1-EECE-3678-20200330204616.JPG | Figure 1 and Figure 2 |
| R1-EECE-3678-20200330204651.JPG | Figure 3 |
| R1-EECE-3678-20200330204715.JPG | Figure 4 |
| R1-EECE-3678-20200330204745.JPG | Figure 5 |
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